{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "Data_titanic1 = pd.read_csv('/Users/yanghongyi/Desktop/titanic/train.csv')\n",
    "Data_titanic2 = pd.read_csv('/Users/yanghongyi/Desktop/titanic/test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <th>PassengerId</th>\n",
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      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
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     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Data_titanic1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
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       "      <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n",
       "      <td>female</td>\n",
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       "   PassengerId  Pclass                                          Name     Sex  \\\n",
       "0          892       3                              Kelly, Mr. James    male   \n",
       "1          893       3              Wilkes, Mrs. James (Ellen Needs)  female   \n",
       "2          894       2                     Myles, Mr. Thomas Francis    male   \n",
       "3          895       3                              Wirz, Mr. Albert    male   \n",
       "4          896       3  Hirvonen, Mrs. Alexander (Helga E Lindqvist)  female   \n",
       "\n",
       "    Age  SibSp  Parch   Ticket     Fare Cabin Embarked  \n",
       "0  34.5      0      0   330911   7.8292   NaN        Q  \n",
       "1  47.0      1      0   363272   7.0000   NaN        S  \n",
       "2  62.0      0      0   240276   9.6875   NaN        Q  \n",
       "3  27.0      0      0   315154   8.6625   NaN        S  \n",
       "4  22.0      1      1  3101298  12.2875   NaN        S  "
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Data_titanic2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1       0.0       3   \n",
       "1            2       1.0       1   \n",
       "2            3       1.0       3   \n",
       "3            4       1.0       1   \n",
       "4            5       0.0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
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     },
     "execution_count": 71,
     "metadata": {},
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    }
   ],
   "source": [
    "Data_titanic = pd.concat([Data_titanic1,Data_titanic2])\n",
    "Data_titanic.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1309 entries, 0 to 417\n",
      "Data columns (total 12 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  1309 non-null   int64  \n",
      " 1   Survived     891 non-null    float64\n",
      " 2   Pclass       1309 non-null   int64  \n",
      " 3   Name         1309 non-null   object \n",
      " 4   Sex          1309 non-null   object \n",
      " 5   Age          1046 non-null   float64\n",
      " 6   SibSp        1309 non-null   int64  \n",
      " 7   Parch        1309 non-null   int64  \n",
      " 8   Ticket       1309 non-null   object \n",
      " 9   Fare         1308 non-null   float64\n",
      " 10  Cabin        295 non-null    object \n",
      " 11  Embarked     1307 non-null   object \n",
      "dtypes: float64(3), int64(4), object(5)\n",
      "memory usage: 132.9+ KB\n"
     ]
    }
   ],
   "source": [
    "Data_titanic.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
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       "      <td>1308.000000</td>\n",
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       "      <td>655.000000</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.294882</td>\n",
       "      <td>29.881138</td>\n",
       "      <td>0.498854</td>\n",
       "      <td>0.385027</td>\n",
       "      <td>33.295479</td>\n",
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       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>378.020061</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.837836</td>\n",
       "      <td>14.413493</td>\n",
       "      <td>1.041658</td>\n",
       "      <td>0.865560</td>\n",
       "      <td>51.758668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.170000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>328.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.895800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>655.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>982.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.275000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1309.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId    Survived       Pclass          Age        SibSp  \\\n",
       "count  1309.000000  891.000000  1309.000000  1046.000000  1309.000000   \n",
       "mean    655.000000    0.383838     2.294882    29.881138     0.498854   \n",
       "std     378.020061    0.486592     0.837836    14.413493     1.041658   \n",
       "min       1.000000    0.000000     1.000000     0.170000     0.000000   \n",
       "25%     328.000000    0.000000     2.000000    21.000000     0.000000   \n",
       "50%     655.000000    0.000000     3.000000    28.000000     0.000000   \n",
       "75%     982.000000    1.000000     3.000000    39.000000     1.000000   \n",
       "max    1309.000000    1.000000     3.000000    80.000000     8.000000   \n",
       "\n",
       "             Parch         Fare  \n",
       "count  1309.000000  1308.000000  \n",
       "mean      0.385027    33.295479  \n",
       "std       0.865560    51.758668  \n",
       "min       0.000000     0.000000  \n",
       "25%       0.000000     7.895800  \n",
       "50%       0.000000    14.454200  \n",
       "75%       0.000000    31.275000  \n",
       "max       9.000000   512.329200  "
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Data_titanic.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "Data_titanic.hist(bins = 50,figsize=(20,15))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 创建测试集\n",
    "def split_train_test(data,test_ratio):\n",
    "    shuffled_indices = np.random.permutation(len(data))\n",
    "    test_set_size = int(len(data) * test_ratio)\n",
    "    test_indices = shuffled_indices[:test_set_size]\n",
    "    train_indices = shuffled_indices[test_set_size:]\n",
    "    return data.iloc[train_indices],data.iloc[test_indices]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1048\n",
      "261\n"
     ]
    }
   ],
   "source": [
    "train_set,test_set = split_train_test(Data_titanic,0.2)\n",
    "print(len(train_set));\n",
    "print(len(test_set));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Survived       1.000000\n",
       "Fare           0.257307\n",
       "Parch          0.081629\n",
       "PassengerId   -0.005007\n",
       "SibSp         -0.035322\n",
       "Age           -0.077221\n",
       "Pclass        -0.338481\n",
       "Name: Survived, dtype: float64"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 相关性度量\n",
    "corr_matrix = Data_titanic.corr()\n",
    "corr_matrix['Survived'].sort_values(ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x13c239710>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x13b596710>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x13b532978>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x13ca82be0>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x13b5e45c0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x13cd5b940>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x13b4cfcc0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x13c9dc048>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x13c9dc0b8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x13c950780>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x13ca13b00>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x13c790e80>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x13b488240>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x13bcd35c0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x13bd04940>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x13bd38cc0>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "from pandas.plotting import scatter_matrix\n",
    "\n",
    "attributes = ['Pclass','Survived','Fare','Age']\n",
    "scatter_matrix(Data_titanic[attributes],figsize=(12,8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1       0.0       3   \n",
       "1            2       1.0       1   \n",
       "2            3       1.0       3   \n",
       "3            4       1.0       1   \n",
       "4            5       0.0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 数据清理\n",
    "Data_titanic.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "Data_titanic['Survived'] = Data_titanic['Survived'].fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1309 entries, 0 to 417\n",
      "Data columns (total 12 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  1309 non-null   int64  \n",
      " 1   Survived     1309 non-null   float64\n",
      " 2   Pclass       1309 non-null   int64  \n",
      " 3   Name         1309 non-null   object \n",
      " 4   Sex          1309 non-null   object \n",
      " 5   Age          1046 non-null   float64\n",
      " 6   SibSp        1309 non-null   int64  \n",
      " 7   Parch        1309 non-null   int64  \n",
      " 8   Ticket       1309 non-null   object \n",
      " 9   Fare         1308 non-null   float64\n",
      " 10  Cabin        295 non-null    object \n",
      " 11  Embarked     1307 non-null   object \n",
      "dtypes: float64(3), int64(4), object(5)\n",
      "memory usage: 132.9+ KB\n"
     ]
    }
   ],
   "source": [
    "Data_titanic.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "median = Data_titanic['Age'].median()\n",
    "Data_titanic['Age'] = Data_titanic['Age'].fillna(median)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1309 entries, 0 to 417\n",
      "Data columns (total 12 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  1309 non-null   int64  \n",
      " 1   Survived     1309 non-null   float64\n",
      " 2   Pclass       1309 non-null   int64  \n",
      " 3   Name         1309 non-null   object \n",
      " 4   Sex          1309 non-null   object \n",
      " 5   Age          1309 non-null   float64\n",
      " 6   SibSp        1309 non-null   int64  \n",
      " 7   Parch        1309 non-null   int64  \n",
      " 8   Ticket       1309 non-null   object \n",
      " 9   Fare         1308 non-null   float64\n",
      " 10  Cabin        295 non-null    object \n",
      " 11  Embarked     1307 non-null   object \n",
      "dtypes: float64(3), int64(4), object(5)\n",
      "memory usage: 132.9+ KB\n"
     ]
    }
   ],
   "source": [
    "Data_titanic.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
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       "      <th>SibSp</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>female</td>\n",
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       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass     Sex   Age  SibSp  Parch     Fare Embarked\n",
       "0            1       0.0       3    male  22.0      1      0   7.2500        S\n",
       "1            2       1.0       1  female  38.0      1      0  71.2833        C\n",
       "2            3       1.0       3  female  26.0      0      0   7.9250        S\n",
       "3            4       1.0       1  female  35.0      1      0  53.1000        S\n",
       "4            5       0.0       3    male  35.0      0      0   8.0500        S"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Data_titanic = Data_titanic.drop(['Name','Ticket','Cabin'],axis = 1)\n",
    "Data_titanic.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 0, ..., 1, 1, 1])"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 将文本标签转化为数字\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "encoder = LabelEncoder()\n",
    "data_sex = Data_titanic['Sex']\n",
    "data_sex_encoder = encoder.fit_transform(data_sex)\n",
    "data_sex_encoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "Data_titanic['Sex'] = data_sex_encoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
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       "      <td>1</td>\n",
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       "      <td>22.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  Sex   Age  SibSp  Parch     Fare Embarked\n",
       "0            1       0.0       3    1  22.0      1      0   7.2500        S\n",
       "1            2       1.0       1    0  38.0      1      0  71.2833        C\n",
       "2            3       1.0       3    0  26.0      0      0   7.9250        S\n",
       "3            4       1.0       1    0  35.0      1      0  53.1000        S\n",
       "4            5       0.0       3    1  35.0      0      0   8.0500        S"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Data_titanic.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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